The prior art of signal classification for both wired and wireless communications make extensive use of digital signal processing. FIG. 1 shows one prior art architecture, described as the Rapid Signal Classifier (RASCL) Tool of CACI International Inc., of Arlington, Va. See, e.g., www.caci.com/mtl/tools/rascl.shtml. RASCL utilizes a RF receiver 10 to demodulate an input signal 11 into an I/Q signal stream. The receiver 10 consists of a low noise amplifier (LNA), down-conversion IQ mixers 12, a phase splitter/shifter element 13 and a synthesizer 16 to generate a Local Oscillator (LO). Then the analog I/Q signals pass through Low Pass Filters (LPFs) 14 and Programmable Gain Amplifiers (PGAs) 18 before being quantized by two high resolution Analog-to-Digital Convertors (ADCs) 22. Signal feature extraction and classification are processed in the digital domain by means of software 24 in a Digital Signal Processor (DSP). Because the RF receiver 10 has no prior knowledge of the received input signals 11, it has to accommodate a large input signal dynamic range (DR). So the high resolution ADCs 22 may need to handle a large dynamic range signal, which is usually more than 14 bits.
FIG. 2 shows an alternative architecture that may reduce the RF receiver complexity and alleviate linearity issues by eliminating down conversion mixers and an analog baseband circuit. Simplification may be achieved at a cost of a higher resolution ADC 26 and require a more sophisticated DSP 24′ by moving most of the analog signal processing done in FIG. 1 into the digital domain. Such a system may have more flexibility since much more of the design is dictated by software than by hardware. FIG. 2 also appears a bit like a Software Designed Radio (SDR) which typically has a DSP for signal reception (but not typically for signal feature extraction and classification).
Signal feature extraction and classification involves processing the received signal to acquire the transmitted data symbol, analyzes the receiving signal or signal mixture composed of multiple signals within a common bandwidth, and extracts the associated signal or signal mixture characteristics varying with time, such as carrier frequencies, occupied RF bandwidth, signal strength, modulation types and data symbol rate etc. Signal classification determines the number of signals and signal types in the received signal mixture based upon the acquired signal features, and then separate them into individual signals. Signal classification also attempts to identify the type of received signal from various features of the received signal. The classification, without implying a limitation, may be that the received signal is a Radar signal, an FM radio signal, a Cell phone signal or a combination of these. The features that allow one to classify a signal may be, without implying a limitation, the frequency of the received signal, the signal strength, the phase of the signal or the Energy Timing Map of the received signal.
There has been a design effort reported in prior publications to incorporate Fast Fourier Transforms (FFT) into analog baseband to acquire the incoming signal features in the early stage to assist the following signal classification and relax the RF transceiver, ADC and DSP design requirements. See, Manel Martinez Ramon et al., “Signal Classification with an SVM-FFT Approach for Feature Extraction in Cognitive Radio,” 2009 SBMO/IEEE MTT-S IMOC. FIG. 3 shows the suggested architecture. This is different from pure digital approaches, since it utilizes both analog and digital processing to achieve the signal feature extraction and classification and potentially reduces the system power consumption by relaxing technical requirements of the critical blocks in the design. But it mandates a complete RF transceiver 10′ to demodulate the incoming RF signal into analog baseband and an analog FFT 28, the design of which is not trivial due to lack of accurately controlled delays in the analog domain. In addition, excessive device mismatch may deteriorate its performance further.
Prior art efforts to classify signals based on the features have relied on sophisticated digital signal processors (DSP) with their inherent size, weight, and cost. A DSP based approach to signal classification may have a probability of correctly classifying a signal greater than 99%. However, there is a need for a lighter, smaller, less expensive signal classification device even at the expense of a reduced probability of correctly classifying a signal.
It is known in the art to use a Super-Regenerative Oscillator (SRO) as an envelope detector. The SRO is normally tuned to a fixed center frequency during one operation. If there is no received signal present at the SRO center frequency, the SRO oscillation start-up process is determined by noise either from the outside environment or the SRO's internal noise, which is preferably small, and thus provides a relatively long start-up time; one the other hand, if there is a signal in the vicinity of the SRO center frequency, the SRO start-up process is accelerated and thus causes the short start-up time to be relatively short. Therefore, the input signal strength corresponds to the SRO start-up time. Further analysis shows the SRO start-up time is inversely proportional to the logarithm of the input signal strength and the SRO holds a dB-to-linear relation between the input signal strength and start-up time. This technique has been applied to receive and demodulate the incoming signals and serve as a receiver to low-rate garage door opener or other radios for many years, either in single ended or differential manner. See, for example, U.S. Pat. No. 6,873,838 Superregenerative oscillator RF receiver with differential output. These and other problems are at least partially solved by the principles of the present invention.